Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for visualizing a performance of a machine-learned model. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by the machine-learned model. The plurality of item representations are arranged according to scores assigned to the plurality of items by the machine-learned model. Further, each of the plurality of item representations is visually configured to represent a label assigned to a corresponding item.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for visualizing a performance of a machine-learned model. An interactive graphical user interface includes an item representation display area that displays a plurality of item representations corresponding to a plurality of items processed by the machine-learned model. The plurality of item representations are arranged according to scores assigned to the plurality of items by the machine-learned model. Further, each of the plurality of item representations is visually configured to represent a label assigned to a corresponding item.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: A collection of data that is extremely large can be difficult to search and/or analyze. Relevance may be dramatically improved by automatically classifying queries and web pages in useful categories, and using these classification scores as relevance features. A thorough approach may require building a large number of classifiers, corresponding to the various types of information, activities, and products. Creation of classifiers and schematizers is provided on large data sets. Exercising the classifiers and schematizers on hundreds of millions of items may expose value that is inherent to the data by adding usable meta-data. Some aspects include active labeling exploration, automatic regularization and cold start, scaling with the number of items and the number of classifiers, active featuring, and segmentation and schematization.

Abstract: Various embodiments provide techniques for graph clustering. In one or more embodiments, a participation graph is obtained that represents relationships between entities. An auxiliary graph is constructed based on the participation graph. The auxiliary graph may be constructed such that the auxiliary graph is less dense than the participation graph and is therefore computationally less complex to analyze. Clusters in the auxiliary graph are determined by solving an objective function defined for the auxiliary graph. Clusters determined for the auxiliary graph may then be utilized to ascertain clusters in the participation graph that solve a related objective function defined for the participation graph.

Abstract: Systems and methods for computing modular polynomials modulo large primes are described. In one aspect, the systems and methods generate l-isogenous elliptic curves. A modular polynomial modulo a large prime p is then computed as a function of l-isogenous elliptic curves modulo p. In one aspect, the modular polynomial may be used in a cryptosystem.

Abstract: Techniques are disclosed for representing and evaluating large prime degree isogenies for use in cryptographic signature and encryption schemes. An isogeny of prime degree 1 may be represented as an ideal in the form (1, A*alpha+B), where 1 comprises the degree of a prime number, the prime number is split into integers a and b, and alpha is a known endomorphism. For a given degree 1, integers a and b define a unique isogeny, allowing the isogeny to be stored with 3 log(1) bits of information. Techniques are also disclosed to evaluate the isogeny at a given point by decomposing the isogeny into an integer and a plurality of smaller degree isogenies, evaluating the smaller degree isogenies at the point with traditional means, and multiplying the results of the evaluations together and with the integer.

Abstract: An exemplary method includes receiving a request to register a peer in a peer-to-peer system; generating or selecting a transaction key for the peer; storing the transaction key in association with registration information for the peer; transmitting the transaction key to the peer and, in response to a request to perform a desired peer-to-peer transaction by another peer, generating a token, based at least in part on the transaction key. Such a token allows for secure transactions in a peer-to-peer system including remote storage of data and retrieval of remotely stored data. Other exemplary techniques are also disclosed including exemplary modules for a peer-to-peer server and peers in a peer-to-peer system.

Abstract: Various embodiments provide techniques for inventory management. In one or more embodiments, a probabilistic model is constructed to represent an inventory of ad impressions available from a service provider. The probabilistic model can be based on a traffic model that describes historic interaction of clients with the service provider using various attributes that define the ad impressions. The probabilistic model provides a distribution of the attributes and relates the attributes one to another based on dependencies. When an order from an advertiser for ad impressions is booked by the service provider, the probabilistic model is updated to reflect an expected probabilistic decrease in the inventory of ad impressions. The updated probabilistic model can then be employed to determine whether the inventory of ad impressions is sufficient to book subsequent orders for ad impressions.

Abstract: Various embodiments provide techniques for inventory clustering. In one or more embodiments, a set of inventory to be processed is placed into an initial cluster. The inventory can be related to impressions for advertising that are defined by values for a set of attributes. Recursive division of the initial cluster is performed by selecting an attribute and deriving child clusters that are constrained by one or more values of the attributes in accordance with one or more clustering algorithms. The clustering algorithms are configured to derive an optimum number of clusters by repetitively generating smaller child clusters and measuring a cost associated with adding additional clusters. Additional child clusters can be formed in this manner until the measured cost to add more clusters outweighs a benefit of adding more clusters.

Abstract: Various embodiments provide techniques for advertisement inventory. In at least some embodiments, a scaled number of impressions can be matched to orders that have scaled impression goals. Impressions can be randomly selected from an offline traffic model and allocated to orders according to a matching algorithm until a number of impression defined by a scale factor is reached. This can occur by sampling the traffic model directly using the scale factor and/or by creating a scaled data set to which the matching algorithm can be applied. The matching algorithm can be configured to identify an order that is farthest away from being complete and then match the randomly selected impression to the identified order. If the scaled orders in the data set can be fulfilled using the scaled number of impressions, a conclusion is made that the original set of orders can be fulfilled using the original impressions.

Abstract: Various embodiments provide techniques for auction flighting. In one or more embodiments, a control group and a test group are designated for participants who compete one to another in online auctions. An inclusive model may then be employed for testing of new conditions for auctions using the groups. In particular, multiple auctions can be conducted and/or simulated, such that control conditions are applied in auctions that do not include at least one member of the test group, and test conditions are applied in auctions having members from both the test group and the control group. A response to the test conditions can then be measured by analyzing behaviors of the participants in the auctions conducted with the control conditions in comparison to behaviors of participants in the auctions conducted with the test conditions.

Abstract: Various embodiments provide techniques for graph clustering. In one or more embodiments, a participation graph is obtained that represents relationships between entities. An auxiliary graph is constructed based on the participation graph. The auxiliary graph may be constructed such that the auxiliary graph is less dense than the participation graph and is therefore computationally less complex to analyze. Clusters in the auxiliary graph are determined by solving an objective function defined for the auxiliary graph. Clusters determined for the auxiliary graph may then be utilized to ascertain clusters in the participation graph that solve a related objective function defined for the participation graph.

Abstract: Efficient data storage and retrieval (e.g., in terms of time and space requirements) is facilitated by implementing an indexing structure comprising an indexing array. That is, a functional relationship between elements of a source set and elements of a query result set can be stored in the indexing structure. This allows, for example, a query regarding whether an element is a member of a set (e.g., whether a particular website or Uniform Resource Locator (URL)) has been visited before) as well as a relationship between the member set and the query (e.g., the number of hyperlinks in the website the last time it was visited) to be resolved efficiently.